2026: Accelerating Discovery Through Conversation
The Pipette.bio Team
December 31, 2025
A New Chapter Begins
As we step into 2026, we find ourselves reflecting on a transformative year for both Pipette.bio and the broader landscape of AI-driven biology. The convergence of large language models, agentic AI systems, and cloud computing has fundamentally changed how scientists interact with their data. At Pipette.bio, we've spent 2025 proving that bioinformatics analysis can be as natural as having a conversation with a knowledgeable colleague.
To our early adopters, beta testers, and the broader research community: thank you. Your feedback, patience, and enthusiasm have shaped every feature we've built. As we enter this new year, we want to share both our reflections on 2025 and our vision for what lies ahead.
Reflecting on 2025: The Year It All Came Together
2025 was the year bioinformatics became conversational. We launched Pipette.bio with a bold premise: that researchers shouldn't need to be programmers to analyze their own sequencing data. What started as a vision has become a working platform that processes real experiments for real scientists every day.
Some highlights from this transformative year:
- Platform Launch: We released Pipette.bio to beta users, enabling conversational access to RNA-seq, single-cell, and multi-omics analysis workflows.
- Tool Integration: We integrated over 50 widely-used bioinformatics tools, from alignment (STAR, HISAT2) to differential expression (DESeq2, edgeR) to single-cell analysis (Seurat, Scanpy).
- Database Connectivity: We connected Pipette to major biological knowledge sources including PubMed, ClinVar, UniProt, GEO, and more, enabling researchers to enrich their analyses with literature and annotations through natural language.
- Reproducibility First: Every analysis on Pipette generates a complete provenance record, capturing inputs, parameters, software versions, and outputs for full reproducibility.
But beyond features, 2025 taught us something more fundamental: the gap between what researchers need and what traditional bioinformatics tools offer is even wider than we anticipated. Scientists don't want to learn command-line syntax or debug Python environments. They want answers to questions like "Which genes are upregulated in my treated samples?" and they want those answers now.
The State of Bioinformatics Heading into 2026
The life sciences are generating data at an extraordinary pace. Single-cell technologies now routinely profile hundreds of thousands of cells per experiment. Spatial transcriptomics is revealing the architecture of tissues at molecular resolution. Long-read sequencing is completing genomes that were once impossible to assemble. And multi-omics approaches are connecting genomics, transcriptomics, proteomics, and metabolomics into integrated portraits of biological systems.
Yet for many researchers, this data abundance has become a bottleneck rather than an opportunity. The analysis queue at the bioinformatics core stretches for weeks. The postdoc who knew R graduated last year. The pipeline that worked on the last dataset throws cryptic errors on the new one.
"2025 proved that bioinformatics can be accessible to everyone who does science, not just those who code. In 2026, we're taking it even further."
This is the problem we've been solving. Not by simplifying biology, but by handling the computational complexity so researchers can focus on the science.
Our Vision for 2026
This year, we're focused on three strategic priorities:
1. Expanding Analysis Capabilities
We're adding support for new analysis domains that our users have requested:
- Spatial transcriptomics: Analyze Visium, Xenium, MERFISH, and other spatial platforms through conversation.
- Long-read sequencing: Process Oxford Nanopore and PacBio data for genome assembly, structural variant detection, and full-length transcript analysis.
- Proteomics integration: Connect mass spectrometry proteomics data with transcriptomic analyses for multi-omics insights.
- CRISPR screen analysis: Identify hits from pooled CRISPR knockout and activation screens.
2. Smarter, More Autonomous Workflows
We're making Pipette more intelligent about understanding research context and suggesting next steps:
- Proactive recommendations: After completing an analysis, Pipette will suggest logical follow-up analyses based on the results.
- Quality-aware processing: Automatic detection and handling of batch effects, outlier samples, and data quality issues.
- Literature-informed analysis: Integration of relevant published findings to contextualize your results against the broader scientific literature.
3. Collaboration and Reproducibility
Science is a team effort, and we're building features to support how research actually happens:
- Shared workspaces: Collaborate with lab members on analyses with shared data and results.
- Publication-ready outputs: Generate figures, methods sections, and supplementary tables formatted for journal submission.
- Analysis templates: Save and share successful workflows as reusable templates for similar experiments.
An Invitation to Build Together
Pipette.bio remains free during our beta period because we believe access to computational tools shouldn't be a barrier to scientific discovery. Whether you're a graduate student running your first RNA-seq experiment, a PI trying to make sense of a collaborator's dataset, or a biotech team scaling up your discovery pipeline, we want to hear from you.
What analyses are you struggling with? What tools do you wish existed? What would make your research life easier? Your feedback directly shapes our roadmap.
Looking Ahead
The promise of AI in science isn't to replace scientists, but to amplify them. To handle the tedious so researchers can focus on the creative. To make the sophisticated accessible. To turn data into discovery faster than ever before.
2026 will be a year of even more rapid progress in AI-driven biology. New foundation models for proteins, molecules, and cells are emerging monthly. Autonomous lab systems are moving from demos to deployments. The boundaries of what's computationally possible are expanding faster than at any point in history.
At Pipette.bio, we're committed to bringing these advances to every researcher's fingertips, not through complexity, but through conversation. We believe that the best interface for biological data analysis isn't a command line or a graphical dashboard. It's a dialogue.
Here's to a year of discovery, collaboration, and pushing the boundaries of what's possible in biological research.
Happy New Year from all of us at Pipette.bio.